Stress test your
retirement plan
A retirement plan that works on average can still fail in practice. Here's how sequence-of-returns risk threatens your savings and how to test your plan against the worst scenarios.
What is sequence-of-
returns risk?
Sequence-of-returns risk is the danger that poor investment returns early in retirement permanently damage your portfolio — even if your long-term average return is perfectly reasonable. It's the single most underappreciated risk in retirement planning, because most calculators assume a steady growth rate and never show you what happens when reality deviates from the average.
The core problem is straightforward: two retirees with identical starting balances, identical withdrawal amounts, and the same average return over 30 years can end up with completely different outcomes. The only difference is when the bad years occur. If the market drops 30% in year 1, you sell shares at depressed prices to cover expenses. Those shares are gone permanently — they never participate in the recovery. If the same 30% drop happens in year 20, the damage is far less severe because you've already funded two decades of retirement.
This is why a single-line projection showing 6% annual growth is dangerously incomplete. It tells you what happens in the average case, but retirement planning failures don't happen in the average case. They happen in the tails — the bad sequences that a straight-line assumption will never reveal. For a deeper look at the mechanics, see our guide to sequence-of-returns risk.
A 30% loss in year 1 on a $1M portfolio = $700K. Withdraw $50K = $650K. Even a 30% gain next year only gets you to $845K. You're permanently behind.
The same 30% loss in year 25 hits a larger base but you've already funded 25 years of retirement. The portfolio has had decades to compound.
The first 5–10 years of retirement are the highest-risk window. Returns in this period determine more about your outcome than any other factor.
A projection that assumes 6% every year will never show you what happens when year 1 returns −22%. That's what stress testing reveals.
Why averages don't
tell the whole story
When someone says "the stock market returns about 7% per year after inflation," they're telling you the truth — and simultaneously hiding the most important detail. That 7% is an average across decades that included individual years of +30%, −40%, and everything in between. The distribution around that average is enormous, and for retirees withdrawing money, the distribution matters far more than the mean.
Consider a plan that "works" when you plug in 6% annual growth. It shows your money lasting to age 92. But that same plan, tested with the actual year-by-year returns of someone who retired in January 2000, may show depletion by age 78. The average return from 2000 to 2020 wasn't dramatically different from the long-term average — but the sequence was devastating. A three-year bear market right at the start, followed by the 2008 financial crisis before the portfolio had recovered. (See our guide to the 4% rule for more on why fixed-rate assumptions fail.), created a compounding withdrawal problem that no amount of subsequent recovery could fix.
The order of returns matters as much as the average. A retirement plan built on averages is a plan that works in theory and may fail in practice. The only way to understand your actual risk is to test against the full range of possible sequences — not just the single smooth line.
Monte Carlo
simulation
Monte Carlo simulation is the most widely used method for testing a retirement plan against uncertainty. Instead of assuming a single growth rate, it runs your plan through 1,000 or more randomized market scenarios. Each scenario picks a different sequence of annual returns drawn from a statistical distribution — typically based on historical market data. Your withdrawals, taxes, Social Security, and inflation-adjusted spending are applied in each simulated year, just as they would be in the real world.
The output is a probability distribution. Rather than telling you "your money lasts to age 88," it tells you "in 87% of scenarios, your money lasts to age 88 or beyond." That percentage is your success rate — the share of simulated futures where your plan survived. The success rate is a far more honest answer than a single projection, because it accounts for the range of market conditions you might actually face.
The real value of Monte Carlo isn't the top-line success rate, though. It's the ability to examine the worst outcomes. What does your portfolio look like in the bottom 10% of scenarios? At what age do those scenarios run out? Understanding the 10th percentile outcome — your worst realistic case — gives you a concrete picture of what failure looks like and how much margin you have.
Each simulation picks random annual returns from a statistical distribution (usually based on historical data). Your withdrawals, taxes, SS, and spending are applied each year. Repeat 1,000+ times.
A 90% success rate means 900 of 1,000 scenarios lasted. The bottom 10% failed. Look at the median outcome AND the 10th percentile — that's your worst realistic case.
85–95% is generally considered solid. Below 80% needs attention. Above 95% may mean you're being too conservative and could spend more or retire earlier.
Historical
stress testing
Historical stress testing takes a different approach than Monte Carlo. Instead of generating random scenarios, it applies actual past market returns to your retirement plan. What would have happened to your specific portfolio — with your balances, your spending, your Social Security start date — if you had retired at the start of the 2008 financial crisis? Or the 2000–2002 dot-com collapse? Or the brutal stagflation of 1973–74?
These aren't hypothetical scenarios. They're documented market history, and they provide an intuitive sense of risk that abstract probabilities sometimes lack. When you see your portfolio balance drop by $280,000 in the first two years and then watch the cascading effect on every subsequent year of your retirement, the concept of sequence risk becomes viscerally real. Historical stress tests answer a question people naturally ask: "What if I retire into a crash like 2008?"
Historical testing only covers scenarios that have already happened. The next crisis may be worse — or entirely different — than anything in the record. That's why Monte Carlo and historical stress testing work best together: Monte Carlo covers the statistical range, historical tests ground you in real-world worst cases. A plan that survives both is far stronger than one tested by either alone.
Test your
plan
A single-line projection shows you the plan. Stress testing shows you what can go wrong — and how much margin you have before things break. The difference between a plan that looks good on paper and one that actually holds up under pressure is the difference between assuming 6% growth every year and asking "what if the first three years are −15%, −22%, and −8%?"
Drawdown Arc's free tier gives you the year-by-year projection: account balances, withdrawals, taxes, Social Security, and RMDs across your entire retirement. That projection is the foundation — you need to see the baseline before you can test against it. Pro adds historical stress testing and Monte Carlo simulation so you can see the range of outcomes, not just the single expected path.
The practical steps are straightforward: enter your actual account balances (Roth, tax-deferred, taxable, cash), your expected spending, your Social Security and any pension income, and your target retirement age. Run the baseline projection first. Then apply stress tests — historical crash scenarios and Monte Carlo randomization — and look at what happens in the bottom 10% of outcomes. If your plan survives those scenarios, you have real confidence. If it doesn't, you know exactly where the vulnerability is and can adjust before it matters.
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